Ontotext

APRIL 10, 2024

Data ecosystems have become jungles and in spite of all the technology, data teams are struggling to create a modern data experience. Bad data tax is rampant in most organizations. Sadly, data quality is losing to data quantity, resulting in “ Infobesity ”. “Any

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. times better performance than the self-managed version.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

Ontotext

FEBRUARY 12, 2024

It also breaks down the code and data monolith and distributes it across the domain teams, which results in better management and scalability. The data mesh concept will mitigate cognitive overload when building data-driven organizations that require intense technical, domain, and operational knowledge.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

CIO Business Intelligence

JUNE 23, 2023

CIOs should prioritize an adaptable technology infrastructure that eliminates data silos, ensures security and governance, and embraces a unified horizontal platform for streamlined data management, reducing integration complexities, skilled workforce requirements, and costs. Data Center Management, IT Strategy

Data Strategy 115

Data Strategy Strategy Visualization Consulting 115

Ontotext

AUGUST 4, 2023

Working across data islands leads to siloed thinking and the inability to implement critical business initiatives such as Customer, Product, or Asset 360. As data is generated, stored, and used across data centers, edge, and cloud providers, managing a distributed storage environment is complex with no map to guide technology professionals.

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52

erwin

FEBRUARY 28, 2024

While the essence of success in data governance is people and not technology, having the right tools at your fingertips is crucial. Technology is an enabler, and for data governance this is essentially having an excellent metadata management tool. Next to data governance, data architecture is really embedded in our DNA.

Metadata 52

Metadata Data Governance Data Quality Technology 52

AWS Big Data

JANUARY 31, 2023

Data governance is increasingly top-of-mind for customers as they recognize data as one of their most important assets. Effective data governance enables better decision-making by improving data quality, reducing data management costs, and ensuring secure access to data for stakeholders.

Data Lake 79

Data Lake Data Governance Data Architecture Data Warehouse 79

Ontotext

NOVEMBER 16, 2023

Data mesh solves this by promoting data autonomy, allowing users to make decisions about domains without a centralized gatekeeper. It also improves development velocity with better data governance and access with improved data quality aligned with business needs.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

CIO Business Intelligence

AUGUST 9, 2022

These include: Generalist: Data engineers who typically work for small teams or small companies wear many hats as one of the few “data-focused” people in the company. These generalists are often responsible for every step of the data process, from managing data to analyzing it. Data engineer job description.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

erwin

DECEMBER 12, 2019

But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance. Managing metadata should not be a sub-goal of data governance. erwin Data Intelligence (erwin DI) combines data management and data governance processes in an automated flow.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

erwin

DECEMBER 12, 2019

But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance. Managing metadata should not be a sub-goal of data governance. erwin Data Intelligence (erwin DI) combines data management and data governance processes in an automated flow.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

Alation

JULY 28, 2022

It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. Cloud data warehouses can provide a lot of upfront agility, especially with serverless databases,” says former CIO and author Isaac Sacolick.

Data Warehouse 69

Data Warehouse Cost-Benefit Data Governance Data-driven 69

Jedox

OCTOBER 22, 2020

In addition to using data to inform your future decisions, you can also use current data to make immediate decisions. Some of the technologies that make modern data analytics so much more powerful than they used t be include data management, data mining, predictive analytics, machine learning and artificial intelligence.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

BI-Survey

DECEMBER 22, 2020

Its availability and quality are crucial for digital success, making it an important economic asset for the business. Data management is unfortunately considered to be a thankless task. Data experts know all too well that their company data is usually not in such good shape. Leverage Your Data.

Data-driven 52

Data-driven Data Strategy Strategy Enterprise 52

Alation

NOVEMBER 2, 2022

It all starts with great management,” Sayle shares. “We’ve And I think this is really a bet on management and their vision for the company.”. Greg Sands is the founder and managing director at Costanoa Ventures, which was the first VC firm to invest in Alation nearly one decade ago. Greg Sands of Costanoa Ventures.

Data Governance 98

Data Governance Marketing Finance Data Lake 98

Peter James Thomas

MAY 3, 2019

Control of Data to ensure it is Fit-for-Purpose. This refers to a wide range of activities from Data Governance to Data Management to Data Quality improvement and indeed related concepts such as Master Data Management. Data Architecture / Infrastructure.

Strategy 55

Strategy Data Architecture Data Quality Data Strategy 55

Peter James Thomas

AUGUST 18, 2018

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Ontotext

FEBRUARY 26, 2024

Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. They have massive amounts of data they’re collecting and storing in their relational databases, document stores, data lakes, and data warehouses.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

Data Virtualization

MAY 19, 2023

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Strategy 52

Data Strategy Strategy Data Integration Management 52

IBM Big Data Hub

APRIL 27, 2022

Data fabric and data mesh are emerging data management concepts that are meant to address the organizational change and complexities of understanding, governing and working with enterprise data in a hybrid multicloud ecosystem. The good news is that both data architecture concepts are complimentary.

Management 89

Management Metadata Data Architecture Data Lake 89

CIO Business Intelligence

JULY 28, 2023

Enterprises are dealing with increasing amounts of data, and managing it has become imperative to optimize its value and keep it secure. Data lifecycle management is essential to ensure it is managed effectively from creation, storage, use, sharing, and archive to the end of life when it is deleted.

Risk 94

Risk Management Cost-Benefit Data mining 94

CIO Business Intelligence

NOVEMBER 29, 2022

A sea of complexity For years, data ecosystems have gotten more complex due to discrete (and not necessarily strategic) data-platform decisions aimed at addressing new projects, use cases, or initiatives. Layering technology on the overall data architecture introduces more complexity. Data and cloud strategy must align.

Data Architecture 110

Data Architecture Data Integration IoT Strategy 110

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure. Pulling it all together.

Data Lake 117

Data Lake Unstructured Data Data Warehouse Data Quality 117

Alation

JANUARY 25, 2022

Data fabric is now on the minds of most data management leaders. In our previous blog, Data Mesh vs. Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. Gartner is explicit: Data catalogs play a foundational role in the data fabric. Simply put?

Metadata 52

Metadata IT Metrics Data-driven 52

CIO Business Intelligence

NOVEMBER 7, 2023

Centralized reporting boosts data value For more than a decade, pediatric health system Phoenix Children’s has operated a data warehouse containing more than 120 separate data systems, providing the ability to connect data from disparate systems. Identify differentiators — and incorporate data discovery.

Data Warehouse 142

Data Warehouse Digital Transformation Data-driven Reporting 142

IBM Big Data Hub

AUGUST 4, 2023

Data democratization is often conflated with data transparency, which refers to processes that help ensure data accuracy and easy access to data regardless of its location or the application that created it. This lets users across the organization treat the data like a product with widespread access.

Data Architecture 65

Data Architecture Data Lake Machine Learning Data Governance 65

Alation

APRIL 19, 2022

Data modernization is the process of transferring data to modern cloud-based databases from outdated or siloed legacy databases, including structured and unstructured data. In that sense, data modernization is synonymous with cloud migration. What Is the Role of the Cloud in Data Modernization?

Cost-Benefit 52

Cost-Benefit Data Governance Manufacturing Data Architecture 52